Machine Learning for Planetary Science (Paperback)

Joern Helbert, Mario D'Amore, Michael Aye, Hannah Kerner

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Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation.

General

Imprint:	Elsevier Science Publishing Co Inc
Country of origin:	United States
Release date:	March 2022
First published:	2022
Editors:	Joern Helbert • Mario D'Amore • Michael Aye • Hannah Kerner
Dimensions:	229 x 152mm (L x W)
Format:	Paperback
Pages:	232
ISBN-13:	978-0-12-818721-0
Categories:	Books > Science & Mathematics > Astronomy, space & time > Theoretical & mathematical astronomy Books > Science & Mathematics > Physics > Applied physics & special topics > Geophysics Books > Professional & Technical > Other technologies > Space science > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	0-12-818721-2
Barcode:	9780128187210